System and method for initial calibration of an air-quality sensor device

ABSTRACT

A system and method for initial calibration of an air-quality sensor device. The method includes establishing at least a reference response to an environmental condition based on a response of at least one calibrated detector to the environmental condition; establishing at least a sensor response to the environmental condition based on the response of at least one uncalibrated detector to the environmental condition; developing, based on the established sensor response, at least a sensor response slope; determining, based on the established reference response, the established sensor response, and the developed sensor response slope, at least a corrected response; and returning the corrected response.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/898,248 filed on Sep. 10, 2019, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to air quality assurance andenergy management and, more particularly, to systems and methods forcalibration of devices for environmental monitoring, advisement, andcontrol of commercial and industrial buildings.

BACKGROUND

While certain improvements in building materials, constructiontechniques, and electronic systems provide for improved efficiency inoperation of a building, energy costs remain among a building operator'sgreatest expenses. Energy costs, such as those costs arising fromheating or cooling a building, may vary greatly based on factors such asresident or tenant environmental preferences, demand-based energypricing, environmental conditions, and the like. Further, buildingoperators may wish to provide for building air quality management andthe comfort of tenants and residents, in addition to cost-optimization,creating a complex balance of energy costs and building requirements, abalance which legacy systems may be ill-equipped to resolve.

Legacy building management systems may provide for the collection ofdata relating to building environmental conditions. Further, such legacysystems may include features providing for control of buildingconditions using devices such as thermostat-linked heating and coolingsystems, remote shutoffs for conduits, and the like. While such legacysystems may provide for environmental control and data collection,legacy means may be ill-suited to high-volume data processing and maylack certain connectivity features useful in reducing operator workloadand maintenance.

In addition to the inefficiencies of legacy systems in managinghigh-volume building data, such systems may fail to provide certainmodern functionalities necessary for efficient building environmentalsystem management. Legacy systems may lack cloud-processing systemswhich provide for collection and analysis of large volumes of buildingdata. Further, legacy systems may lack the interconnectivity required tocollect energy market and grid data and to respond to such dataautomatically. In addition, legacy systems may require manualcalibration and re-calibration of sensor and regulator devices, wheremanual calibration and re-calibration may require a prohibitively largeoutlay of time and effort on the part of building management andmaintenance teams.

Further, legacy systems may fail to provide for management of buildingwellness controls. Building wellness controls include those controlsconcerning health and wellness parameters of building management, suchas air filter status, air quality status, and the like, as opposed tocomfort controls which may be directed to provision of comfortableconditions for building occupants, such as controls directed to airtemperatures. Further, legacy systems may require various trade-offsbetween wellness, comfort, and energy efficiency to reach variousbuilding management goals. As a result, legacy systems may lack thecapacity to provide optimized wellness, comfort, and energy solutions,requiring intensive manual adjustment of wellness, comfort, and energyparameters to reach desired building control states.

In addition to legacy building management systems, certain smaller-scalecomfort and energy management systems may provide similarfunctionalities in smaller-scope applications, such as homes, smalloffices, and the like. Examples of smaller-scale comfort and energymanagement systems include the Nest® home management system by Google®.While smaller-scale systems provide for certain energy and comfortcontrol functionalities, such systems lack the scope of applicationrequired to provide centrally-managed control of wellness, comfort, andenergy needs for larger buildings such as office towers, retail stores,and the like, as such buildings may include large numbers of managementdevices, complex connectivity architectures, predefined communicationprotocols, and the like. Further, smaller-scale systems may be primarilydirected to management of comfort and energy parameters, to theexclusion of integrated safety and wellness controls. As a result,smaller-scale management systems may fail to provide functionalities andscopes of application required for use in large-scale building systemsmanagement applications.

It would therefore be advantageous to provide a solution that wouldovercome the challenges noted above.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor to delineate the scope of anyor all aspects. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term “someembodiments” or “certain embodiments” may be used herein to refer to asingle embodiment or multiple embodiments of the disclosure.

Certain embodiments disclosed herein include a method for initialcalibration of an air-quality sensor device. The method comprisesestablishing at least a reference response to an environmental conditionbased on a response of at least one calibrated detector to theenvironmental condition; establishing at least a sensor response to theenvironmental condition based on the response of at least oneuncalibrated detector to the environmental condition; developing, basedon the established sensor response, at least a sensor response slope;determining, based on the established reference response, theestablished sensor response, and the developed sensor response slope, atleast a corrected response; and returning the corrected response.

Certain embodiments disclosed herein also include a non-transitorycomputer readable medium having stored thereon causing a processingcircuitry to execute a process, the process comprising: establishing atleast a reference response to an environmental condition based on aresponse of at least one calibrated detector to the environmentalcondition; establishing at least a sensor response to the environmentalcondition based on the response of at least one uncalibrated detector tothe environmental condition; developing, based on the established sensorresponse, at least a sensor response slope; determining, based on theestablished reference response, the established sensor response, and thedeveloped sensor response slope, at least a corrected response; andreturning the corrected response.

Certain embodiments disclosed herein also include a system for initialcalibration of an air-quality sensor device. The system comprises: asensor array, the sensor array containing a plurality of sensor sockets;an input/output (I/O) circuitry; a processing circuitry; and a memory,the memory containing instructions that, when executed by the processingcircuitry, configure the system to: establish at least a referenceresponse to an environmental condition based on a response of at leastone calibrated detector to the environmental condition; establish atleast a sensor response to the environmental condition based on theresponse of at least one uncalibrated detector to the environmentalcondition; develop, based on the established sensor response, at least asensor response slope; determine, based on the established referenceresponse, the established sensor response, and the developed sensorresponse slope, at least a corrected response; and return the correctedresponse.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out anddistinctly claimed in the claims at the conclusion of the specification.The foregoing and other objects, features, and advantages of thedisclosed embodiments will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 is a network diagram utilized to describe a system for managementof building air quality assurance, comfort wellness, and energymanagement, according to an embodiment.

FIG. 2 is a flowchart depicting a method for sensor initial calibration,according to an embodiment.

FIG. 3 is an illustration depicting sensor and reference responses,according to an embodiment.

FIG. 4 is an illustration depicting a device for initial calibration ofsensors, according to an embodiment.

FIG. 5 is a flowchart depicting a method for over-the-air (OTA) sensorre-calibration, according to an embodiment.

FIG. 6 is an example schematic diagram of a command and control server(CCS), according to an embodiment.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

FIG. 1 is an example network system 100 depicting the variousembodiments for management of building air quality assurance, comfort,wellness, and energy management, according to an embodiment. A networksystem 100 may include one or more components such as, as examples andwithout limitation, a command and control server (CCS) 110, a dataupload server (DUS) 115, a network 120, one or more bridge devices 130-1through 130-N (hereinafter, “bridge device” 130 or “bridge devices”130), as well as one or more sensor devices 140-1 through 140-N(hereinafter, “sensors” 140 or “sensor” 140). In an embodiment, a bridgedevice 130 is a building air quality assurance, comfort, or energymanagement device.

The CCS 110 is configured to provide for cloud-level administration ofone or more bridge devices 130, sensor devices 140, and the like, aswell as any combination thereof. The CCS 110 may be also configured tocommunicate with one or more components of the network system 100 viathe network 120, according to the means described hereinbelow. The CCS110 may include various processing, memory, and networking components,and the like, allowing the CCS 110 to execute instructions and providedata processing, including those methods or processes describedhereinbelow. Further, the CCS 110 may be configured to include one ormore databases, data repositories, or other, like, storage components,where the included storage components may be physical, virtual, or anycombination thereof. The CCS may be configured to include, withoutlimitation, a most recent values database 114, a configuration database112, and the like, as well as any combination thereof, as describedhereinbelow. The CCS 110 may be implemented as physical hardware, assoftware virtualizing physical hardware, or as a combination of physicaland virtualized components. In an embodiment, the CCS 110 may beconfigured to operate as a data upload server (DUS) 115, as describedhereinbelow. A detailed view of a CCS, according to an embodiment, isprovided with respect to FIG. 6 , below.

The most recent values database 114 provides storage for register data,as may be collected according to the processes described hereinbelow,organized by order of receipt. Registers may include data featuresdescribing, variously, device and component operating systems,device-specific data features, user-defined features, and the like.Register data may include data relating to the operating system orsystems of various components of a network system 100.

In addition, register data may include data relating to the operation ofvarious components of the sensor and bridge devices, describingcomponent-specific data features, such as, as an example and withoutlimitation, the firmware version of a specific sensor. Further, registerdata may include user register data, where user register data is datarelevant to a user's custom purpose. As an example, user register datamay include custom-generated instruction sets for personalizing sensordevice data output formats. In addition, register data may include,without limitation, data relevant to device identification andmanufacture, such as the firmware version of a given sensor device 140,the serial number of a given sensor device 140, lot and batch numbers ofa given sensor device 140, and the like, as well as any combinationthereof.

The configuration database 112 provides storage for system configurationinstructions and collected data. Data which may be stored in theconfiguration database 112 includes, as examples and without limitation,sensor calibration data, building management system interface data,updated code images, various factory commands, DUS assignments, marketand environmental data, register updates, device-specific commands,other, like, data, and any combination thereof.

The data upload server (DUS) 115, is a cloud-level server providing forcollection and storage of data from one or more bridge devices 130,sensor devices 140, connected building management systems (not shown),and the like, as well as any combination thereof. The DUS 115 may be aresident server, located and operated on-location where the one or morebridge devices 130 and sensor devices 140 are deployed. Further, the DUS115 may be a hosted server, located off-site and, in an embodiment,hosted by a platform configured to provide cloud computing, storage, andother, like, services, including, as examples and without limitation,Amazon® Web Services, Microsoft® Azure, and other, like, platforms.

The DUS 115 may include one or more storage or memory components, suchas the time series database 117, and the like. The time series database117 may be configured to store data received or collected according tothe methods described hereinbelow, as well as other, like, methods. Datastored in the DUS 115, including data stored in the time series database117, may be encrypted, unencrypted, or partially-encrypted. Further,stored data may be compressed to various degrees and may be stored toone or more physical partitions or other, like, separated storageallocations. The DUS 115, and any sub-component thereof, may beimplemented as physical hardware, as software virtualizing physicalhardware, or as a combination of physical and virtualized components.Further, the DUS 115 may be interconnected with the various componentsof the network system 100, via the network 120, according to the variousmeans described hereinbelow.

The network 120 provides interconnectivity between the variouscomponents of the network system 100. The network 120 may be, but is notlimited to, a wireless, cellular, or wired network, a local area network(LAN), a wide area network (WAN), a metro area network (MAN), theInternet, the worldwide web (WWW), similar networks, and any combinationthereof. The network may be a full-physical network, includingexclusively physical hardware, a fully-virtual network, including onlysimulated or otherwise virtualized components, or a hybridphysical-virtual network, including both physical and virtualizedcomponents. Further, the network 120 may be configured to encrypt data,both at rest and in motion, and to transmit encrypted, unencrypted, orpartially-encrypted data.

The bridge devices 130 are devices configured to serve as intermediariesbetween the sensor devices 140 and the various components of the networksystem 100. The bridge devices 130 may be configured to, as examples andwithout limitation, collect data from various sensor devices 140, toupload or push instructions to the various sensor devices 140, to uploador push collected data to the various components of the network system100, to collect, from the various components of the network system 100,various instructions, to collect data, independent of the various sensordevices 140, to execute instructions, and the like, as well as anycombination thereof.

The bridge devices 130 may be configured to connect with the variouscomponents of the network system 100 via the network 120, according tothe means described hereinabove. Further, the bridge devices 130 may beconfigured to connect with the various sensor devices 140 via wired orwireless means, or any combination thereof, such as those describedhereinabove, via those protocols described with respect to the network120, as well as other, like, protocols, or any combination thereof. Inaddition, the bridge devices 130 may be configured to encrypt data, bothat rest and in motion, and to transmit data with varying degrees ofencryption.

The bridge devices 130 may be configured to execute instructions andprocess data relevant to the various components of the system viaconnections to the various devices according to various pre-definedcommunication protocols, patterns, exchange systems, and the like,including via the network 120, as described hereinabove. An exampleimplementation of a bridge device 130 is further disclosed in U.S.patent application Ser. No. 17/015,892 filed herewith, assigned to thecommon assignee, and the contents of which are hereby incorporated byreference.

The sensor devices 140 are devices configured to provide for collectionof data pertinent to building air quality, comfort, wellness, and energymanagement. The sensor devices 140 may be include one or more sensorssuch as, as examples and without limitation, thermometers and gasdetection and measurement sensors, hygrometers, and the like, as well asany combination thereof. Further, sensor devices 140 may be configuredto collect data and to transmit collected data to the various componentsof the network system 100 via the bridge devices 130, to executeinstructions received, via the bridge devices 130, from the variouscomponents of the network system 100, to execute other, like, functions,as well as any combination thereof. The sensor devices 140 may beconfigured to connect with the bridge devices 130 via those meansdescribed hereinabove. In a preferred embodiment, the sensor devices 140and a bridge device 130 securely communicate over a wireless mediumusing an air-quality communication protocol. In an embodiment, thesensor devices 140 may be configured to connect directly with thenetwork 120, via those means described herein, without connection to abridge device 130. An example implementation of a sensor device 140 isfurther disclosed in U.S. patent application Ser. No. 17/015,918 filedherewith, assigned to the common assignee, and the contents of which arehereby incorporated by reference.

FIG. 2 is an example flowchart 200 depicting a method for initialcalibration of a sensor device 140, according to an embodiment. One ormore of the steps described with respect to the method 200 for sensorinitial calibration may be performed using a calibration deviceincluding, without limitation, the device described with respect to FIG.4 , below. The method described with respect to FIG. 2 may be executedby one or more components of a network system, such as the networksystem, 100, of FIG. 1 , above, including, without limitation, the CCS110, and the like, as well as any combination thereof.

At S210, reference responses are established. Reference responses aredata outputs collected in response to environmental conditions, wheresuch environmental conditions may be those conditions relevant to thecollection of environmental data by a sensor device, including, withoutlimitation, temperature, humidity, particulate and gas concentrations,light levels, other, like, conditions, and any combination thereof.Reference responses may be collected from one or more calibrated sourcesincluding, without limitation, test gasses, including ambient gasconcentrations measured via a calibrated device, calibrated measurementdevices, and the like, as well as any combination thereof.

Reference responses may be established, based on one or moreenvironmental condition data points, according to one or morepre-defined or user-defined rules. Relevant environmental condition datamay include, without limitation, building or space occupancy, knownbuilding ventilation settings, current system date or time, variousoutside references, such as weather reports, and the like, as well asother, like, condition data, and any combination thereof. Further,reference responses may be established based on one or more provided orcollected data features including, without limitation, data featuresdescribing environmental conditions, as may be collected from one ormore building management systems (BMSs), or various components orsub-components thereof.

As a first example, reference responses may be established, for a carbondioxide sensor, where room occupancy is low and where ventilation is setto a level standard for the location, providing for collection of carbondioxide sensor data at conditions approaching ambient conditions whichhave, for example, known concentrations of carbon dioxide (CO₂).Further, according to the same example, such ambient conditions may bevalidated by comparison with one or more factors, known to variouscomponents of a network system, such as the CCS, bridge, and sensordevices, of FIG. 1 , above, and the like, where such factors mayinclude, without limitation, calendar dates, such as holidays andweekends, other, like, data, and any combination thereof, providing forcollection of reference values for ambient environmental conditions,where such reference values may be considered as reference valuescollected with respect to a known reference gas.

Further, as a second example, reference responses may be established foran ozone sensor with respect to a known building air-flushing schedule,providing for collection of reference responses to outside air enteringthe sample space during the air-flushing process, where such referenceresponses may be further confirmed by comparison with data collected byweather stations and air-monitoring stations.

In an additional example, collection of reference response data may besuspended where outside references, such as weather reports, indicate ahigh degree of smog or smoke in an area, providing for collection onlyof standard reference response data for application in establishingreference responses. Further, according to the same example, one or morenetwork system components, such as the CCS, bridge, or sensor devices,and the like, as well as any combination thereof, of FIG. 1 , above, maybe configured to average multiple outside reference values and report asingle, averaged outside reference value, as well as to filter outlyingvalues and perform other, like, data analysis and filtering processeswith regard to collected outside reference data.

In a further example, reference responses may be established based onreference response data provided by a BMS, providing for establishmentof reference responses based on “authoritative” BMS-supplied referencevalues including, without limitation, temperature, relative humidity,and the like, as well as any combination thereof.

In an additional example, reference responses may be establishedconditionally based on the inclusion of one or more sensor types, suchas barometric sensors, providing for establishing reference responsesbased on data collected from such sensors, such as by including anadditional term in an absolute humidity reference response calculation.

Reference responses may be raw-data responses, such as measurements ofvoltage, current, resistance, and the like, as may be collected fromcalibrated sources in response to environmental conditions. Wherereference responses are raw-data responses, such responses may beconverted into measured values by application of one or more scalingfactors, conversion algorithms, and the like, configured to representraw-data responses in formats relevant to the various sensor typesincluding, without limitation, temperatures, light levels, humidityvalues, particulate and gas concentrations, and the like. Further,reference responses may be established for one or more measurementpoints including, without limitation, high and low measurements, as wellas other, like, points.

At S220, sensor responses are established. Sensor responses are dataoutputs collected from un-calibrated sensors in response to the same orsimilar environmental conditions described with respect to S210. Sensorresponses may be established by the same means described with respect toS210, including by collection of raw-data responses, from one or moresensor devices. In addition, as described with respect to S210, raw-dataresponses may be subsequently converted into sensor-type measurementvalues such as, as examples and without limitation, temperatures,relative humidity values, light levels, gas and particulateconcentrations, and the like. Further, sensor responses may beestablished with respect to one or more environmental conditions,including high and low measurements, as well as other, like,measurements. Further, sensor responses may be established for one ormore sensors entering an inspection process, for one or more sensorsincluded in finished devices, and the like, as well as any combinationthereof.

At S230, sensor response slopes are developed. Sensor response slopesare measures of a sensor's reported measurement values, where sensorreported measurement values are represented as linear interpolations ofnon-linear functions, such as quadratic equations, measured with respectto the condition to which the sensor is exposed, such as, for example, acalibrated gas with a known concentration. Sensor response slopes may bedeveloped accordion to the following equation:

$M_{sense} = \frac{\left( {S_{HR} - S_{LR}} \right)}{\left( {A_{H} - A_{L}} \right)}$

In the above equation, where the sensor response is described as alinear approximation, the sensor response slope, M_(sense), is given asthe quotient of a numerator, representing a sensor value rise, dividedby a denominator, representing a sensor value run, wherein the numeratoris equal to the highest reported sensor value, given as S_(HR), less thelowest reported sensor value, given as S_(LR), and where the denominatoris equal to the highest actual value, A_(H), less the lowest actualvalue, A_(L).

As an example, the linear sensor response slope for a temperature sensormay be determined by collecting two sensor measurements at known,respective room temperatures of sixty and fifty degrees. Where thesensor reports high and low values of sixty-five and forty-five degrees,respectively, the sensor response slope may be determined according tothe above equation. According to the same example, the numerator appliedto the above equation may be equal to twenty degrees, determined bysubtracting the lowest reported sensor value of forty-five degrees fromthe highest reported sensor value of sixty-five degrees. Further,according to the same example, the denominator applied to the aboveequation may be equal to ten degrees, determined by subtracting thelowest actual value of fifty degrees from the highest actual value ofsixty degrees. As a result, according to the same example, the sensorresponse slope may be equal to two, determined by dividing twentydegrees by ten degrees, as described hereinabove. According to the sameexample, where the sensor response slope is equal to two, such a valuemay indicate that the change in the sensor's response is double theactual change in the property or environmental condition detected by thesensor.

Further, S230 may include development of reference response slopes.Reference response slopes may be determined according to the equationdescribed with respect to determination of sensor response slopes, withmodifications included to address reference response highs and lowsinstead of sensor device response highs and lows. As described withrespect to S210, reference response slope data may be collected from oneor more calibrated sources.

At S240, corrected responses are determined. A corrected response is asensor calibration correction configured to provide for calibration of asensor device based on sensor and reference response slopes andestablished data, as described hereinabove. Corrected sensor responsesmay be determined according to the following equation:

${Sensor}_{corrected} = {\left( {\delta{Sensor}_{reported}*\frac{M_{reference}}{M_{sensor}}} \right) + R_{LR}}$

According to the above equation, a sensor correction is equal to the sumof the reference device low reported measurement, R_(LR), as establishedat S210, added to the product of the quotient of the reference responseslope, M_(reference), divided by the sensor response slope, M_(sense),both as determined at S230, multiplied by the difference between sensorand reference reported values, or the offset, given asδSensor_(reported). The offset value is determined by subtracting thesensor low response value, determined at S220, from the reference lowresponse value, determined at S210.

At S250, corrected responses are returned. Corrected responses, asdetermined at S240, as well as component parameter values, as describedhereinabove, may be returned by one or more means including withoutlimitation, storage to one or more sensor devices for calibration, byuploading and persisting corrected responses to one or more location forsubsequent use and tracking, other, like, means, and any combinationthereof. Further, corrected responses may be returned by display orpresentation of sensor responses via one or more means including,without limitation, display through a screen or display included in adevice, return as a print-out or other, like, format, and the like, aswell as any combination thereof. In addition, according to anembodiment, returning corrected responses at S250 may further includelabeling of individual sensors with identifying information including,without limitation, serial numbers, batch numbers, lot numbers, and thelike, as well as any combination thereof, and returning such identifyinginformation as described hereinabove. Stored corrected responses,corrected response parameter values, and sensor serial, lot, and batchnumbers, may be stored to one or more memory or storage components,including the memory or storage components described with respect to thenetwork system, 100, of FIG. 1 , above, providing for subsequentanalysis of such data for purposes including, without limitation,execution of one or more over-the-air (OTA) re-calibration methods, suchas that described with respect to FIG. 5 , below. Further, storedcorrected responses, and the various component parameters thereof, maybe, at the execution of S250, correlated to a given sensor device andstored to one or more memory components, wherein such memory componentsmay be communicably connected with the given sensor device to which thestored corrected responses are correlated.

In addition, returning corrected responses at S250 may include returningmultiple corrected responses for a single sensor device. Multiplecorrected responses may be generated, according to the methods describedhereinabove, for a single sensor device based on multiple distinctcorrection response parameters, such as slopes and offsets, as describedhereinabove. Multiple correction response parameters may be collectedaccording to the methods described hereinabove, across multipleiterations of a sensor initial calibration process. Where multiplecalibration response parameters are collected, the collected responseparameters may be stored and applied to the generation of multiplecorrected responses, providing for the generation of multiple correctedresponses for the same sensor device, as may be applicable to theidentification of one or more ideal or preferred corrected responses.

It should be noted that the initial calibration is performed on anydetector or sensor component in the sensor device. Such detectors mayinclude, without limitation, a carbon dioxide (CO₂) detector, aparticulate matter detector, a formaldehyde (CH₂O) detector, a carbonmonoxide (CO) detector, a relative humidity detector, an ozone (O₃)detector, a wide-band total volatile organic compound (TVOC) detector, alight level detector, a narrow-band or high-sensitivity TVOC detector,and the like.

FIG. 3 is an illustration 300 depicting sensor and reference responses,according to an embodiment. The illustration 300 provides a visualdepiction of the relationships between the quantities noted with respectto FIG. 3 , above. The illustration 300 includes a reported referencelow value 310, a reported reference high value 320, a reported sensorlow value 330, a reported sensor high value 340, and an offset value350.

The reported reference low value 310 is a value reported by a calibratedsource, as described hereinabove, for an environmental condition with aknown low value, such as a minimum temperature in a temperature-testingschedule. Further, the reported reference low value 310 may be thelowest value which the given calibrated source can measure and report.The reported reference high value 320 is a value reported by acalibrated source, also as described hereinabove, for an environmentalcondition with a known high value, such as a maximum temperature in atemperature-testing schedule. In addition, the reported reference highvalue 320 may be the highest value which the given sensor can measureand report.

The reported sensor low value 330 is a value reported by an uncalibratedsensor, such as a sensor undergoing a calibration process as describedwith respect to FIG. 3 , above, for an environmental condition with aknown low value. Further, the reported sensor low value 330 may be thelowest value which the given sensor can measure and report. Similarly,the reported sensor high value 340 is a value reported by anuncalibrated sensor for an environmental condition with a known highvalue. In addition, the reported sensor high value 340 may be thehighest value which the given sensor can measure and report. The offsetvalue 350 is a value describing the difference between the reportedreference low value 310 and the reported sensor low value 330.

FIG. 4 is an illustration depicting a device for initial calibration ofsensors, according to an embodiment. the calibration device 410 is adevice providing for low-cost means for validating, measuring, or bothvalidating and measuring sensor offset and slope parameters, asdescribed hereinabove. Further, the calibration device 410 may beconfigured to provide means for retaining sensor samples in stateswherein periodic measurements may be automated. Operation of acalibration device may include, in addition to the method described withrespect to FIG. 2 , above, sensor sampling across intervalscorresponding to sensor production batches or lots. The calibrationdevice may include a microcontroller 420, an input/output (I/O) circuit430, a memory 440, a sensor array 450, and a plurality of sensor sockets451-1 through 451-N (hereinafter, “sensor socket” or “sensor sockets”).In an embodiment, a calibration device 410 may further include one ormore removeable memory devices (not shown), configured to store variouscalibration parameters, as described herein, and further configured tobe disconnected from a calibration device 410 and connected to otherdevices, including sensor devices, for purposes including, withoutlimitation, transferring data between the calibration device 410 andother devices, for other, like, purposes, and any combination thereof.

The sensor array 450 includes sensor sockets 451, configured to connectwith one or more sensor or detector components, as may be include in asensor device, such as, as examples and without limitation, carbondioxide (CO₂) detectors, particulate matter detectors, formaldehyde(CH₂O) detectors, carbon monoxide (CO) detectors, relative humiditydetectors, ozone (O₃) detectors, wide-band total volatile organiccompound (TVOC) detectors, light level detectors, narrow-band orhigh-sensitivity TVOC detectors, and the like.

In an embodiment, the calibration device 410 may be configured toprovide one or more sensor device burn-in functionalities. A sensordevice burn-in functionality may be, without limitation, a calibrationdevice 410 mode of operation configured to, as examples and withoutlimitation, identify defective sensor devices, normalize sensor deviceoperations from a production state to an operation state, and the like,as well as any combination thereof. Where a calibration device 410 isconfigured to provide burn-in functionality for identifying defectivesensor devices, the calibration device 410 may be configured to energizeone or more sensor devices, connected through the sensor sockets 451,within pre-defined operating power levels, and to maintain sensor deviceenergization for a given period of time, providing for identification ofdefective sensor devices by detection of devices which “burn-out” orfail before reaching a predetermined burn-in period length.

Further, where a calibration device 410 is configured to provide burn-infunctionality for normalizing sensor operations, the calibration device410 may be configured to provide, to the connected sensor devices, powerat one or more levels, providing for adjustment of sensor deviceoperations from as-manufactured states to operational states by “aging”the energized sensor devices, including adjustment of sensor devicereadings, for sensors in the same lot, toward a median value. As anexample of a normalizing functionality, sensors which mis-reportenvironmental conditions when first manufactured, by either under- orover-reporting, may be “aged” to an operational state by energizing thesensor devices until sensor device readings for multiple devicesconverge at a median value. In addition, a normalizing functionality mayinclude an additional burn-in cycle following the aging of sensordevices to the described median value, providing for the adjustment ofthe described median value to a value representing a medianun-calibrated sensor reading, based on a provided environmentalcondition.

A calibration device 410 may be configured to provide for calibration ofone or more types of sensor devices. A calibration device 410 mayinclude, in addition to sensor sockets 451, one or more means forconnection with various sensor devices including, without limitation,pin-type connectors, universal serial bus (USB) connectors, pure-signalelectrical connectors, such as “alligator clips,” and the like, as wellas any combination thereof.

A calibration device 410 may be configured to provide environmentaltesting conditions, exposing connected sensors to one or more conditionsincluding, without limitation, various temperatures, air pressures,relative humidity levels, gas and particulate exposures, light levels,and the like, as well as any combination thereof. As examples, andwithout limitation, a calibration device 410 may be configured tocalibrate sensors including temperature sensors, relative humiditysensors, light level sensors, air pressure sensors, particulate and gasdetectors, and the like, as well as any combination thereof.

The microcontroller 420 is a processing circuitry configured to executeinstructions, including instructions relevant to those processesdescribed herein, and other, like, processes, and may be realized as oneor more hardware logic components and circuits. For example, and withoutlimitation, illustrative types of hardware logic components that can beused include field programmable gate arrays (FPGAs),application-specific integrated circuits (ASICs), Application-specificstandard products (ASSPs), system-on-a-chip systems (SOCs), graphicsprocessing units (GPUs), tensor processing units (TPUs), general-purposemicroprocessors, microcontrollers, digital signal processors (DSPs), andthe like, or any other hardware logic components that can performcalculations or other manipulations of information.

The I/O circuit 430 is a hardware component configured to provideconnectivity between the calibration device and various other devicesand systems including, without limitation, the network system, 100, ofFIG. 1 , above, and various components thereof. The I/O unit may beconfigured to provide wireless connectivity by means including, withoutlimitation, ethernet, serial I/O, universal serial bus (USB), and thelike, via wireless means including, without limitation, Bluetooth,infrared, Wi-Fi, and the like, as well as any combination of wired andwireless means.

The memory 440 is a component providing for temporary, semi-permanent,or permanent storage of one or more data features related to theoperation of the calibration device 410 including, without limitation,sensor and reference response values, sensor corrections, short-termvalues applicable to execution of various calculations, other, like,data features, and any combination thereof. The memory 440 may bevolatile (e.g., random access memory, etc.), non-volatile (e.g., readonly memory, flash memory, etc.), or a combination thereof.

In one configuration, software for implementing one or more embodimentsdisclosed herein may be stored in a storage (not shown). In anotherconfiguration, the memory 440 is configured to store such software.Software shall be construed broadly to mean any type of instructions,whether referred to as software, firmware, middleware, microcode,hardware description language, or otherwise. Instructions may includecode (e.g., in source code format, binary code format, executable codeformat, or any other suitable format of code). The instructions, whenexecuted by the microcontroller 420, cause the microcontroller 420 toperform the various processes described herein.

FIG. 5 is an example flowchart 500 depicting a method for calibration ofan air-quality sensor device, according to an embodiment. The method500, in an embodiment, may be executed by a remote server, such as a CCS(e.g., CSS 110, FIG. 1 ). In an embodiment, the method 500 includesre-calibration of the initial calibration parameters set for the sensordevice at the factor. The re-calibrated parameters' values are sent tothe sensor device over-the-air (OTA). The calibration method describedwith respect to FIG. 5 may be applicable to calibration of one or moresensors or detectors, as may be included in a sensor device, such as, asexamples and without limitation, carbon dioxide (CO₂) detectors,particulate matter detectors, formaldehyde (CH₂O) detectors, carbonmonoxide (CO) detectors, relative humidity detectors, ozone (O₃)detectors, wide-band total volatile organic compound (TVOC) detectors,light level detectors, narrow-band or high-sensitivity TVOC detectors,and the like.

At S510, sensor data is received from one or more sensor devices. Foreach sensor, the data includes metadata describing sensor device'sattributes, personalization requests, as well as data features relatedthe sensor's recorded sensor readings. Sensor metadata may include,without limitation, sensor device, serial number, lot, batch number,location, and type. The metadata may further include data featurescollected by one or more sensor devices, and the like, as well as anycombination thereof. Data features collected by one or more sensordevices may include instantaneous and time-delayed sensor readings,charts, graphs, tables, and other, like, data features describing sensortrends, patterns, and the like, in environmental readings, various dataregisters relevant to collected sensor data, other, like, data features,and any combination thereof.

A personalization request may be a persistent sensor value offset,providing of overriding adjustment of one or more collected sensormeasurements. A personalization request may be generated by a user, suchas through a sensor device interface, automatically, including by one ormore BMS systems or components thereof, and the like, as well as anycombination thereof. As an example, a personalization request mayinclude a persistent offset, set by a user through a sensor device,providing for sensor under-reporting by specification of a “minus fivedegrees” offset for a temperature sensor. Sensor data may be receivedfrom one or more sensor or bridge devices including, without limitation,the sensor devices, 140, and bridge devices, 130, of FIG. 1 , above.

At S520, the received sensor data is analyzed. Received data may beanalyzed to identify one or more patterns, trends, andstatistically-significant values in the received data, and the like.Patterns, trends, and the like, as may be identified at S520, mayinclude periodic fluctuations of sensor reading data, such astemperature cycles occurring over the course of a day, long-term sensordrift patterns, such as increases in sensor reading values over a spanof months, despite no change in a sensor's environmental conditions,other, like, patterns and trends, and any combination thereof.

In an example embodiment, statistically-significant values may include,without limitation, minimum, maximum, mean, and median sensor values, aswell as various distributions and analyses thereof, and the like, aswell as any combination thereof. Received data may be analyzed byapplication of one or more algorithms, methods, processes, and the like,configured to identify trends, patterns, and statistically-significantvalues in received sensor data.

In an additional embodiment, analysis at S520 may further includeanalysis configured to identify long-term and short-term sensor drift.Sensor drift describes the tendency of a sensor device to, with time,report values which differ from values expected from aproperly-calibrated sensor. Sensor drift may include a time-dependency,whereby sensor reported values may differ from expected values to agreater degree with increasing time separation from a most-recentcalibration update. Sensor drift may be short-term drift, applicable todrift phenomena across relatively short time periods, such as minutes,hours, or days. Further, sensor drift may be long-term drift, applicableto drift phenomena across longer time periods, such as weeks, months,and years. It may be understood that the respective time periodsdescribed with respect to short-term and long-term drift are provided asexamples, and that other, like, time periods may be applicable withoutloss of generality or departure from the scope of the disclosure.

Short-term drift and long-term drift may be identified, during analysisat S520, by analysis of data derived from one or more sensor readingsincluding, without limitation, analysis of sensor reading distributionsfor similar devices, as well as tendencies of such distributions to“drift” away from an initial median over time. Sensor readings may beanalyzed to determine current and historical reported values, trends inreported values over time, and the like, as well as any combinationthereof. Drift may be identified by correlation of current andhistorical reported values, and trends relative thereto, with referencevalues obtained by one or more means including, as examples and withoutlimitation, long-term sampling of control sensors from the same lot orbatch, retained at the point of calibration or manufacture to providereference values for drift detection, sampling of other, deployed,sensors of the same lot or batch, comparison with expectation valuesderived based on known sensor properties and drift patterns, and thelike, as well as any combination thereof. Further, identification ofdrift may include comparison of sensor readings with one or morethreshold values, wherein the threshold values may be determined basedon the reference values described hereinabove, including thresholdvalues determined by application of one or more algorithms, methods, orthe like to the various reference values. In addition, identification ofdrift may include identification of drift based on comparison of sensordrift patterns with one or more threshold patterns or values, whereinsuch threshold patterns or values may be determined according to methodssimilar or identical to those described hereinabove. Where drift isidentified at S520, identification of mis-calibrated sensors at S530 mayinclude identification of sensors reporting drift values, andcomputation of re-calibration updates at S540 may include computation ofupdates including corrections to adjust for identified drift.

At S530, mis-calibrated sensors are identified. Mis-calibrated sensorsare sensors reporting data values which differ from expected reportingvalues by amounts exceeding one or more pre-defined thresholds.Mis-calibrated sensors may be identified by determination of, based onthe results of the analysis performed at S520 and without limitation,sensors reporting values above or below expected values by amountsexceeding given thresholds, sensors regularly reporting anomalousvalues, such as weekly temperature spikes exceeding predefinedthresholds, sensors reporting values which differ, by more than apredefined threshold, from values reported by co-located sensors of thesame type, other, like, determinations, and any combination thereof. Inaddition, sensors may be identified as mis-calibrated where a sensor'scurrent or historical drift, or drift patterns, exceeds pre-determinedthresholds. Further, in an embodiment, sensor devices may be identifiedas mis-calibrated where one or more sensor devices have not been thetargets of a re-calibration process, such as that defined with respectto FIG. 5 , for more than a predefined time period, such as sensorswhich have not been re-calibrated within the previous calendar month. Inan additional embodiment, sensors may be manually identified asmis-calibrated by an administrator or other user, including manualidentifications through various sensor devices, bridge devices, CCSs,and other, like, components of a network system, such as the networksystem, 100, of FIG. 1 , above.

At S540, re-calibration updates are computed. Re-calibration updates areupdates configured to calibrate a sensor device such that the sensordevice's reported readings fall within a range of expected values. Sucha range of expected values is based on a reference reported value, towhich is applied a pre-defined statistical deviation, providing for arange of expected reading values. Re-calibration updates may betransmitted in a form scripts, firmware updates, and other, like,modifications of sensor device software or application configured toprovide for over-the-air (OTA) sensor device calibration.

Re-calibration updates may include updates configured to adjust theinitial calibration data, such as the offset, slope, and other, like,parameters of a sensor device's corrected response, as described withrespect to FIG. 2 , above. Further, re-calibration updates may includeupdates configured based on sensor drift value, as determined based ondata analyzed at S320, including updates configured to subtract such asensor drift value from a current sensor reading value.

Computation of re-calibration updates may include identification ofsensor drift for one or more sensors of the same lot, batch, type, andthe like. Sensor lots, batches, and the like, may be identified based onone or more lot or batch identifiers, such as those generated asdescribed with respect to FIG. 2 , above. Identification of lot or batchdrift may include comparison of average or median values for all sensorswithin a given lot or batch with one or more reference values, asdescribed hereinabove, to identify one or more lots, batches, or other,like, groups of sensors which include sensors reporting anomalousenvironmental reading data. Where computing of re-calibration updatesincludes such lot or batch analyses, re-calibration update computationmay further include computation of one or more updates, as describedhereinabove, by automatic generation of scripts, firmware updates, andthe like, targeted to specific sensor batches, lots, or serial numbers,and configured to update some or all of the sensors in a given lot orbatch to report readings consistent with expected values derived fromreference data, as described hereinabove.

Further, computation of re-calibration updates at S540 may includeidentification of one or more sensor devices to be excluded fromre-calibration or to receive individual, one-off re-calibration updates.Sensor devices may be identified as excluded from re-calibration orsubject to one-off updating based on one or more factors including,without limitation, sensor device location, sensor device ambientconditions, such as may be applicable to sensor devices placed inlocations for which, in normal conditions, a sensor may report a valuewhich would be considered anomalous, other, like, factors, and anycombination thereof.

In addition, sensor devices may be identified as excluded fromre-calibration or subject to one-off updating based on one or moreconditional triggers, including by delaying a prepared re-calibrationupdate until a point at which a room or space is unoccupied, bypreparing a one-off update when a room is unoccupied, despite otherupdate schedules or triggers, by preparing a one-off update including anoffset different than that applied to a device of the same type in adifferent location, other, like, location and occupancy-relatedconditions, and any combination thereof. Further, sensor devices may beidentified as subject to one-off updating based on one or more signalsreceived from various components of a network system, and varioussystems connected therewith, including, without limitation, a BMS. Wherea sensor device is identified as subject to one-off updating based onsignals received from a BMS, including signals specifying that one ormore BMS purge or reset processes are in progress, S540 may includecomputation of a one-off update for the identified sensor device ordevices, despite other update schedules, update triggers, and the like.Where a sensor device is identified as being subject to a one-offre-calibration update, computation of re-calibration updates at S540 mayinclude computation of re-calibration updates configured to provide forspecial re-calibration of a sensor device to provide for adjustedexpected reading values.

In an embodiment, computation of re-calibration updates at S540 mayinclude computation of updates based on manually-supplied re-calibrationdata. Manually-supplied re-calibration data may be collected from one ormore sources including, without limitation, user input components, suchas touchscreens, included in sensor devices, network controllers, suchas the CCS 110, of FIG. 1 , above, other, like, sources, and anycombination thereof. Computation of updates based on manually-suppliedre-calibration data may include, without limitation, generation of oneor more corrected responses, as described hereinabove, based onuser-specified or administrator-specified offset values, slope values,other, like, parameter values, and any combination thereof. Further,computation of updates based on manually-supplied re-calibration datamay include computation of updates targeting specific, specifieddevices, computation of updates applicable according to variousschedules or triggers specified by a user or administrator, and thelike, as well as any combination thereof. In addition, where computationof updates is based on manually-supplied re-calibration data,computation at S540 may include computation of one or more updates,computed as described, for other, like sensor devices, as well asautomatic restriction or rejection of updates based on manually-supplieddata, where such data is determined, according to one or morepre-defined rules, to be improper or invalid. Where S540 includescomputation of updates based on manually-supplied re-calibration data,S540 may be executed at any point prior to the execution of S550,including concurrently with, or to the exclusion of, S510, S520, orS530, without loss of generality or departure from the scope of thedisclosure.

At S550, re-calibration updates are pushed. Pushing re-calibrationupdates may include execution, by a CCS or other, like device, one ormore commands or instructions configured to send, to one or more bridgedevice, sensor devices, or both, re-calibration updates prepared atS540. Further, the commands or instructions executed at S540 may beconfigured to execute, in a target sensor or bridge device, one or moreprocesses for updating device firmware, device operating code, and thelike, providing for time-of-push updates to bridge and sensor devicecalibration parameters. Further, in an embodiment, pushingre-calibration updates at S550 may include, without limitation, manualentry of updated calibration parameters, such as calibration slopes oroffsets, by an administrator or other user, through one or morecomponents of a network system, such as bridge devices, sensor devices,and the like.

In an additional embodiment, re-calibration updates may be stored andpushed in response to one or more push-trigger conditions including, asexamples and without limitation, one or more pre-defined or user-definedpush delay waiting periods, one or more user-defined or pre-definedcalendars or schedules, one or more update content triggers, as may betriggered where an update includes or excludes a specified value, one ormore user or administrator push commands, and the like, as well as anycombination thereof. As an example, according to the same embodiment, are-calibration update push-trigger may be configured to provide forpushing updates where a system date or time falls between twopredetermined dates or times, and where the contents of a registerincluded in the update includes a given value.

Further, pushing re-calibration updates at S550 may include creation andstorage of one or more re-calibration records. Re-calibration recordsmay be data features describing the various re-calibration updatespushed at S550. Re-calibration records may include, without limitation,re-calibration update values, such as updated slopes, offsets, and thelike, target device serial, lot, and batch numbers, target devicelocations, re-calibration update date and time, re-calibration updatestatus, such as whether the update was successfully pushed, other, like,data, and any combination thereof. Re-calibration records created atS550 may be stored to one or more memory or storage componentsincluding, without limitation, those memory or storage components orsub-components of the network system, 100, of FIG. 1 , above, and thelike, as well as any combination thereof, providing for subsequentre-calibration update auditing and lot-tracing. Re-calibration recordsmay be created or stored on a per-update basis, relevant to multipledevices re-calibrated by the same update, a per-unit basis, relevant toindividual devices, other, like, bases, and any combination thereof.

FIG. 6 is an example schematic diagram of a command and control server(CCS) 110, according to an embodiment. The CCS 110 includes a processingcircuitry 610 coupled to a memory 620, a storage 630, and a networkinterface 640. In an embodiment, the components of the CCS 110 may becommunicatively connected via a bus 650.

The processing circuitry 610 may be realized as one or more hardwarelogic components and circuits. For example, and without limitation,illustrative types of hardware logic components that can be used includefield programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), Application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), graphics processing units (GPUs),tensor processing units (TPUs), general-purpose microprocessors,microcontrollers, digital signal processors (DSPs), and the like, or anyother hardware logic components that can perform calculations or othermanipulations of information.

The memory 620 may be volatile (e.g., random access memory, etc.),non-volatile (e.g., read only memory, flash memory, etc.), or acombination thereof.

In one configuration, software for implementing one or more embodimentsdisclosed herein may be stored in the storage 630. In anotherconfiguration, the memory 620 is configured to store such software.Software shall be construed broadly to mean any type of instructions,whether referred to as software, firmware, middleware, microcode,hardware description language, or otherwise. Instructions may includecode (e.g., in source code format, binary code format, executable codeformat, or any other suitable format of code). The instructions, whenexecuted by the processing circuitry 610, cause the processing circuitry610 to perform the various processes described herein.

The storage 630 may be magnetic storage, optical storage, and the like,and may be realized, for example, as flash memory or another memorytechnology, compact disk-read only memory (CD-ROM), Digital VersatileDisks (DVDs), or any other medium which can be used to store the desiredinformation.

The network interface 640 allows the CCS 110 to communicate with thevarious components, devices, and systems described herein forcalibration of building air quality assurance devices, as well as other,like, purposes.

It should be understood that the embodiments described herein are notlimited to the specific architecture illustrated in FIG. 6 , and otherarchitectures may be equally used without departing from the scope ofthe disclosed embodiments.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various othersensor units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

It should be understood that any reference to an element herein using adesignation such as “first,” “second,” and so forth does not generallylimit the quantity or order of those elements. Rather, thesedesignations are generally used herein as a convenient method ofdistinguishing between two or more elements or instances of an element.Thus, a reference to first and second elements does not mean that onlytwo elements may be employed there or that the first element mustprecede the second element in some manner. Also, unless statedotherwise, a set of elements comprises one or more elements.

As used herein, the phrase “at least one of” followed by a listing ofitems means that any of the listed items can be utilized individually,or any combination of two or more of the listed items can be utilized.For example, if a system is described as including “at least one of A,B, and C,” the system can include A alone; B alone; C alone; 2A; 2B; 2C;3A; A and B in combination; B and C in combination; A and C incombination; A, B, and C in combination; 2A and C in combination; A, 3B,and 2C in combination; and the like.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the disclosed embodiment and the concepts contributed by the inventorto furthering the art, and are to be construed as being withoutlimitation to such specifically recited examples and conditions.Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosed embodiments, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof. Additionally, it is intended that such equivalentsinclude both currently known equivalents as well as equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

What is claimed is:
 1. A method for initial calibration of anair-quality sensor device, comprising: establishing at least a referenceresponse to an environmental condition based on a response of at leastone calibrated detector to at least two different known values of theenvironmental condition; establishing at least a sensor response to theenvironmental condition based on the response of at least oneuncalibrated detector to at least two further different known values ofthe environmental condition wherein at least one of the two furtherdifferent known values of the environmental condition is different fromat least one of the at least two different known values of theenvironmental condition; developing, based on the established sensorresponse, at least a sensor response slope; determining, based on theestablished reference response, the established sensor response, and thedeveloped sensor response slope, at least a corrected response; andreturning the corrected response.
 2. The method of claim 1, wherein theenvironmental condition is any one of: temperature, relative humiditylevel, air pressure level, light level, gas concentration, andparticulate concentration.
 3. The method of claim 1, whereinestablishing the reference response further comprises: establishing alow reference reported value corresponding to a lowest of the twodifferent known values of the environmental condition; and establishinga high reference reported value corresponding to a highest of the twodifferent known values of the environmental condition.
 4. The method ofclaim 1, wherein establishing the sensor response further comprises:establishing a low sensor reported value corresponding to a lowest ofthe two further different known values of the environmental condition;and establishing a high sensor reported value corresponding to a highestof the two further different known values of the environmentalcondition.
 5. The method of claim 1, wherein the reference response andsensor response are established based on the same environmentalcondition.
 6. The method of claim 1, further comprising: re-performingthe initial calibration of the air-quality sensor device at a predefinedschedule.
 7. The method of claim 1, wherein the response slope iscomputed as:$M_{reference} = \frac{\left( {R_{HR} - R_{LR}} \right)}{\left( {A_{Hr} - A_{Lr}} \right)}$wherein S_(HR) is a highest sensor response generated by the at leastone uncalibrated detector in response to a highest valued one A_(H) ofthe at least two further different known values of the environmentalcondition and S_(LR) is a lowest sensor response generated by the atleast one uncalibrated detector in response to a lowest one A_(L) of theat least two further different known values of the environmentalcondition; a reference response slope is computed as:$M_{sense} = \frac{\left( {S_{HR} - S_{LR}} \right)}{\left( {A_{H} - A_{L}} \right)}$wherein R_(HR) is a highest response generated by the at least onecalibrated detector in response to a highest valued one A_(HR) of the atleast two different known values of the environmental condition andR_(LR) is a lowest response generated by the at least one calibrateddetector in response to a lowest one A_(Lr) of the at least twodifferent known values of the environmental condition; and wherein thecorrected response Sensor_(corrected) is determined as${Sensor_{corrected}} = {\left( {\delta Sensor_{reported}*\frac{M_{reference}}{M_{sensor}}} \right) + R_{LR}}$where δSensor_(reported) is determined by subtracting the sensor lowresponse S_(LR) from the reference low response R_(LR) when A_(L) andA_(Lr) are substantially the same.
 8. The method of claim 1, wherein thereference response is established, based on one or more environmentalcondition data values according to at least one defined rule.
 9. Themethod of claim 1, wherein returning the corrected response furthercomprises: labeling the corrected response, and a corresponding sensordevice, with features identifying the sensor device; and burning-in thesensor device with the labeled corrected response and identifyingfeatures.
 10. The method of claim 9, wherein burning-in the sensordevice further comprises: adjusting sensor reading values toward amedian value by energizing the sensor device for a pre-determined timeperiod; and adjusting the median value toward an expected value byfurther energizing the sensor device for a second pre-determined timeperiod.
 11. A method for initial calibration of an air-quality sensordevice, the process comprising: establishing at least a referenceresponse to an environmental condition based on a response of at leastone calibrated detector to the environmental condition; establishing atleast a sensor response to the environmental condition based on theresponse of at least one uncalibrated detector to the environmentalcondition; developing, based on the established sensor response, atleast a sensor response slope; determining, based on the establishedreference response, the established sensor response, and the developedsensor response slope, at least a corrected response; and returning thecorrected response; wherein returning the corrected response furthercomprises: labeling the corrected response, and a corresponding sensordevice, with features identifying the sensor device; burning-in thesensor device with the labeled corrected response and identifyingfeatures; and wherein burning-in the sensor device further comprises:adjusting sensor reading values toward a median value by energizing thesensor device for a pre-determined time period; and adjusting the medianvalue toward an expected value by further energizing the sensor devicefor a second pre-determined time period.
 12. A system for initialcalibration of an air-quality sensor device, comprising: a sensor array,the sensor array containing a plurality of sensor sockets; aninput/output (I/O) circuitry; a processing circuitry; and a memory, thememory containing instructions that, when executed by the processingcircuitry, configure the system to: establish at least a referenceresponse to an environmental condition based on a response of at leastone calibrated detector to at least two different known values of theenvironmental condition; establish at least a sensor response to theenvironmental condition based on the response of at least oneuncalibrated detector to at least two further different known values ofthe environmental condition wherein at least one of the two furtherdifferent known values of the environmental condition is different fromat least one of the at least two different known values of theenvironmental condition; develop, based on the established sensorresponse, at least a sensor response slope; determine, based on theestablished reference response, the established sensor response, and thedeveloped sensor response slope, at least a corrected response; andreturn the corrected response.
 13. The system of claim 12, wherein theenvironmental condition is any one of: temperature, relative humiditylevel, air pressure level, light level, gas concentration, andparticulate concentration.
 14. The system of claim 12, wherein thesystem is further configured to: establish a low reference reportedvalue corresponding to a lowest of the two different known values of theenvironmental condition; and establish corresponding to a highest of thetwo different known values of the environmental condition.
 15. Thesystem of claim 12, wherein the system is further configured to:establish a low sensor reported value corresponding to a lowest of thetwo further different known values of the environmental condition; andestablish a high sensor reported value corresponding to a highest of thetwo further different known values of the environmental condition. 16.The system of claim 12, wherein the reference response and sensorresponse are established based on the same environmental condition. 17.The system of claim 12, wherein the system is further configured to:re-perform the initial calibration of the air-quality sensor device at apredefined schedule.
 18. The system of claim 12, wherein the responseslope is computed as:$M_{sense} = \frac{\left( {S_{HR} - S_{LR}} \right)}{\left( {A_{H} - A_{L}} \right)}$wherein S_(HR) is a highest sensor response generated by the at leastone uncalibrated detector in response to a highest valued one A_(H) ofthe at least two further different known values of the environmentalcondition and S_(LR) is a lowest sensor response generated by the atleast one uncalibrated detector in response to a lowest one A_(L) of theat least two further different known values of the environmentalcondition; a reference response slope is computed as:$M_{reference} = \frac{\left( {R_{HR} - R_{LR}} \right)}{\left( {A_{Hr} - A_{Lr}} \right)}$wherein R_(HR) is a highest response generated by the at least onecalibrated detector in response to a highest valued one A_(HR) of the atleast two different known values of the environmental condition andR_(LR) is a lowest response generated by the at least one calibrateddetector in response to a lowest one A_(Lr) of the at least twodifferent known values of the environmental condition; and wherein thecorrected response Sensor_(corrected) is determined as${Sensor}_{corrected} = {\left( {\delta{Sensor}_{reported}*\frac{M_{reference}}{M_{sensor}}} \right) + R_{LR}}$where δSensor_(reported) is determined by subtracting the sensor lowresponse S_(LR) from the reference low response R_(LR) when A_(L) andA_(Lr) are substantially the same.
 19. The system of claim 12, whereinthe system is further configured to: label the corrected response, and acorresponding sensor device, with features identifying the sensordevice; and burn-in the sensor device with the labeled correctedresponse and identifying features.
 20. The system of claim 19, whereinthe system is further configured to: adjust sensor reading values towarda median value by energizing the sensor device for a pre-determined timeperiod; and adjust the median value toward an expected value by furtherenergizing the sensor device for a second pre-determined time period.